Download Coordinating Voting in American Presidential and House Elections by July 21, 1997

Coordinating Voting in American Presidential and House Elections
by
Walter R. Mebane, Jr.
y
July 21, 1997
y Associate Professor, Department of Government, Cornell University, [email protected].
Building on earlier work (Alesina 1987; 1988; Alesina and Rosenthal 1989), Alesina and Rosenthal (1995) use a unidimensional spatial voting model in which individuals coordinate their votes to
explain important political and economic phenomena in the United States, including in particular
the midterm loss phenomenon and election-related uctuations in economic growth. Coordination
means that an individual's presidential and congressional vote choices are jointly determined. Assuming that each individual's voting strategy satises a condition they call conditional sincerity,
Alesina and Rosenthal derive a pivotal voter theorem from which it follows that if the two political
parties oer distinct policy positions, then in equilibrium some voters will split their tickets, voting for one party for President but for the other party for the legislature. Alesina and Rosenthal
also show conditions under which some voters will switch their votes between the presidential and
midterm elections. Such switching, they argue, can explain why the President's party uniformly
loses vote share at midterm. Alesina and Rosenthal consider complications due to incumbent
advantage, and they derive economic implications in the form of an expected partisan business
cycle (1995, 137{203). The latter they test empirically using aggregate time series data (Alesina,
Londregan and Rosenthal 1993; Alesina and Rosenthal 1995, 204{242).
The theory that Alesina and Rosenthal develop is elegant, and they amass a persuasive body of
aggregate time series evidence to support its key predictions. What is missing is micro-level evidence
to show that individuals do indeed make their voting decisions in accordance with strategies of the
kind that Alesina and Rosenthal posit.
In this paper I describe and estimate a probabilistic voting model designed to test whether
individuals' votes for President and for the House of Representatives are coordinated as Alesina
and Rosenthal's theory suggests they should be. In particular I test for the most immediate
consequence of the pivotal voter theorem, which is that each voter should use a cutpoint strategy
(Alesina and Rosenthal 1995, 77, 111{113). With the spatial competition assumed to be occurring
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over a continuous set of liberal-to-conservative policy alternatives, mapped onto the unit interval
[0; 1], the assertion is that the interval contains two cutpoints, ^ and ~. An individual whose policy
bliss point is less than ^ always votes for the Democratic candidate for President while someone
whose bliss point is greater than ^ always votes for the Republican. Someone whose bliss point
is less than ~ always votes Democratic in the legislative election while someone whose bliss point
is greater than ~ always votes Republican. When ^ 6= ~, the theorem says that individuals whose
bliss points fall between the two cutpoints will split their tickets between the two parties.
In the probabilistic coordinating voting model developed in this paper, someone whose bliss
point is less than ^ has a greater probability of voting for the Democratic presidential candidate
than does someone whose bliss point is greater than ^, but the vote choice is not certain to be for
the Democrat. Votes for House candidates are likewise probabilistic. I assume that the cutpoints
themselves are random variables about which each individual has a subjective probability distribution. Each person's probabilistic coordinating voting behavior occurs relative to the expected
values the cutpoints have according to the person's subjective distribution for them. I do not assume that the dierences between an individual's bliss point and the expected cutpoints are the
sole determinants of her vote choices. The individual's partisanship and evaluation of the economy
also aect the choices, as do spatial comparisons not mediated by the cutpoint values. House votes
are also aected by whether the incumbent is running for reelection.
I augment the specication of coordinating voting with one idea about the substantive content
of coordination that is not considered in Alesina and Rosenthal's (1995) theory. The idea is that
someone who believes the economy is poor or getting worse may coordinate in a dierent way
than does someone who thinks the economy is in great shape. The person with the negative view
may be more likely to wish to see government policy pushed in a more expansive direction|to see
active steps taken to create jobs and to provide help for those encountering bad luck during the
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tough period. Someone who does not believe that the economic situation is so dire may instead
believe that the usual American norm of personal economic responsibility (Brody and Sniderman
1977; Schlozman and Verba 1979) should apply and so favor a more restricted range of government
actions. Someone who has a negative view of the economy may therefore coordinate his votes in a
way that favors Democratic candidates|one or both cutpoints may be shifted upward|while the
economic optimist may coordinate in a way more favorable to Republican candidates.
In this paper I examine votes only in presidential election years, using data from the American
National Election Studies (ANES) Pre- and Post-Election Surveys of 1980, 1984, 1988, 1992 and
1996. The analysis tests Alesina and Rosenthal's predictions regarding on-year coordinating voting
but not their predictions about vote switching between presidential and midterm years.
Subjective Cutpoint Distributions
Using the pivotal voter theorem, Alesina and Rosenthal (1995, 96) show the legislative and presidential cutpoints to be related to one another according to
~ = P (^) 1 +RK + (1 , P (^)) 1D++KK
(1)
where R and D are the policy positions of the two parties (0 D R 1), P (^) is the
probability that the Republican presidential candidate wins given the presidential cutpoint ^, and
K = (1 , )(R , D ) for some 2 (0; 1). In Alesina and Rosenthal's theoretical development,
\ represents the weight of the president in policy formation" (1995, 47). If = 1, the president
dictates policy and the legislature plays no role. If = 0, the legislature determines policy and the
president is irrelevant. Using ~R = R =(1 + K ) and ~D = (D + K )=(1 + K ), equation (1) becomes
~ = P (^)(~R , ~D ) + ~D :
(2)
The presidential cutpoint satises the constraint ~D ^ ~R .
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I treat the party policy positions, the cutpoints and the probability that the Republican presidential candidate wins all as subjective and therefore as potentially varying across individuals.
Each individual i may have a distinct belief about the policies likely to ensue given a Democratic
rather than a Republican President. Each individual acts in response to subjective values ~Ri and
~Di , 0 ~Di ~Ri 1, rather than in response to a pair of universally shared values ~R and
~D . Such variation may occur because individuals may have diering beliefs about the relative
power of the presidential and legislative institutions. In this case, i varies over individuals so that
Ki = (1 , i )(R , D ), ~Ri = R =(1 + Ki) and ~Di = (D + Ki )=(1 + Ki). Or individuals may
have diverse beliefs about the party policy positions. For instance, beliefs may vary because of
variations across legislative districts in the policy positions taken by elected party representatives.
Individuals do not know the exact values of the cutpoints, but rather make their choices based on
their beliefs about the cutpoints' statistical distributions. Individual i believes that the presidential
cutpoint has a continuous distribution function on the interval [~Di ; ~Ri ], with probability density
R
f^ i (^) > 0 for all ^ 2 [~Di ; ~Ri ] and ~~DiRi f^ i (^)d^ = 1. In particular, I assume that each individual
treats ^ as having a beta distribution dened by f^ i (^) = fGi (gi (^)), where gi (^) = (^, ~Di )=(~Ri ,
~Di ) and fGi is a beta density on [0; 1], namely
fGi (g ) = g Gi ,1(1 , g)Gi ,1 =B (
0g1;
Gi ; Gi );
where Gi > 0, Gi > 0, and B ( ; ) denotes the beta function with arguments and . For
individual i the presidential cutpoint ^ is therefore distributed beta ( Gi ; Gi ) on [~Di ; ~Ri ]. The
cutpoint value that individual i uses for making voting decisions is the expectation
Z ~Ri
^i = ~ ^f^ i (^)d^
"Di
#
Z gi (~Ri )
=
gi(^)fGi (gi (^))dgi(^) (~Ri , ~Di) + ~Di
gi (~Di )
= gi(~Ri , ~Di ) + ~Di
(3)
4
R
where gi = 01 gfGi (g )dg .
In order to minimize the losses it expects from errors in predicting ^, each individual i should
choose the form for f^ i that has the smallest variance for a given value of ^i . Among beta densities,
the minimum variance occurs if f^ i is unimodal.1 f^ i is unimodal if fGi is unimodal, and fGi is
unimodal if Gi > 1 and Gi > 1. I use Gi = exp(b1xi + b2) + 1 and Gi = exp(b2) + 1, where xi
is an observed variable and b1 and b2 are coecients.2
Similarly to Alesina and Rosenthal's formulation, an expected legislative cutpoint ~i for individual i is determined jointly with the expected presidential cutpoint ^i . In the present model
the cutpoints are related to one another through the density f^ i and a function Pi (^) that represents the subjective (according to i) conditional probability that the Republican candidate will
win, given a presidential cutpoint at ^. Individual i computes Pi (^) by integrating over the range
of voters that ought to be voting for the Republican presidential candidate given ^. The subjective
distribution of voter bliss points over which i integrates is a beta ( Vi ; Vi ) distribution on [0; 1],
with Vi > 0, Vi > 0 and density fVi (v ) = v Vi ,1 (1 , v )Vi ,1 =B ( Vi ; Vi ) for 0 v 1. I dene
R
Pi (^) = ^1 fVi (v)dv. Because Pi (^) is a decreasing function of ^ for all ^ 2 [0; 1], such a form for
Pi satises the requirement that a higher value for the presidential cutpoint should imply a lower
probability that the Republican candidate wins (Alesina and Rosenthal 1995, 95). Using a beta distribution for each individual's beliefs about the distribution of voters makes those beliefs compatible
with Alesina and Rosenthal's (1995, 73) assumption that there is a continuum of voters described
by a cumulative distribution function that is continuous and strictly increasing on (D ; R). The
beta distribution generalizes Alesina and Rosenthal's (1995, 86) uniform distribution.3
The value that i uses for the legislative cutpoint is the expected value of Pi (^)(~Ri , ~Di ) + ~Di ,
with the integration being over the values of ^ that have positive probability according to f^ i ,
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namely
Z ~Ri
~i = ~ [Pi(^)(~Ri , ~Di ) + ~Di ]f^ i (^)d^
Di
= Pi (~Ri , ~Di ) + ~Di ;
(4)
R~
where Pi = ~DiRi Pi (^)f^ i (^)d^. Notice that equation (4) has the same form as equation (2).
I assume that each individual's subjective distribution for voters' preferences is compatible
with the individual's subjective distribution for the presidential cutpoint, in the sense that if fVi
places the bulk of voters in some range (Vi ; Vi ), then by fGi it is highly likely that the transformed
presidential cutpoint gi (^) is in that same range.4 Such a compatibility assumption follows naturally
from the idea that each individual i believes both that the set of voters capable of deciding the
presidential election is contained in the interval [~Di ; ~Ri ], and that all voters are using cutpoint
strategies. I assume that fGi and fVi have the same mode. Letting Vi = exp(b3xi )+1 and Vi = 1,
with xi being the same as in Gi , I impose the assumption by specifying that b3 = b1.5
The Coordinating Structure
A key property of the \stable conditionally sincere" equilibria for Alesina and Rosenthal's models
is that the voter whose bliss point i equals the legislative cutpoint ~ is indierent between the
parties in the legislative election, and likewise the voter whose bliss point equals the presidential
cutpoint ^ is indierent between the parties in the vote for President (Alesina and Rosenthal 1995,
73{82, 107-117). I impose analogous indierence requirements in the present model. If i equals the
expected legislative cutpoint ~i , then the coordinating structure has no eect on individual i's vote
for a House candidate, and if i equals the expected presidential cutpoint ^i , then the coordinating
structure has no eect on individual i's presidential vote.
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Dene the coordinating structure as the set wi = fwiRR; wiDR; wiRD ; wiDD g, where
wiRR = aP (i , ^i) + aH (i , ~i )
wiDR = aP (^i , i) + aH (i , ~i ) , aPH (i , ^i )(i , ~i )
wiRD = aP (i , ^i) + aH (~i , i ) , aPH (i , ^i )(i , ~i )
wiDD = aP (^i , i) + aH (~i , i )
with aP 0, aH 0 and aPH 0 being constant coecients. The probability jk
i that i votes for
party j 2 fD; Rg for President and party k 2 fD; Rg for the House is a function of vijk = wijk + zijk ,
where zijk includes factors that aect the vote choice in addition to the coordinating structure. I
assume that jk
i is multinomial, with
jk
i =
exp vijk
;
exp viRR + exp viDR + exp viRD + exp viDD
Using vote indicator variable yPi = 1 if i votes for the Republican presidential candidate, = 0 if i
votes for the Democrat, and yHi = 1 if i votes for the Republican House candidate, = 0 if i votes
for the Democrat, the likelihood for i is6
y y
DR (1,y )y
RD y (1,yHi )(DD )(1,yPi )(1,yHi ) :
Li = (RR
i ) Pi Hi (i ) Pi Hi (i ) Pi
i
The coordinating structure species that, net of the eects represented by the zijk terms, a
split-ticket vote is most likely when an individual's bliss point i is between the two cutpoints.
Suppose the zijk terms are all zero. Then if aP = aH > 0, the odds (DR + RD )=(RR + DD ) of
a split ticket are maximized at i = (^i + ~i )=2. The value of i that maximizes the odds of a split
ticket is closer to ^i if aP > aH and closer to ~i if aH > aP , but the maximizing value always falls
between ^i and ~i . Because log(DR =RD ) = 2[aP (^i , i ) + aH (i , ~i )] + ziDR , ziRD , the content
of a split vote depends on the relative position of the cutpoints, sharply so if aP and aH are large.
If ^i > ~i , then a split vote by i is more likely to be for the Democratic presidential candidate and
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a Republican House candidate, while if ~i > ^i the vote is more likely to be for the Republican
presidential candidate and a Democratic House candidate.
Data
To estimate the model I use data from the ANES Pre-/Post-Election surveys of 1980, 1984, 1988,
1992 and 1996 (Miller and the National Election Studies 1982; 1986; 1989; Miller, Kinder, Rosenstone and the National Election Studies 1992; Rosenstone, Kinder, Miller and the National Election
Studies 1997). For the current analysis I pool the cross-sections from all ve years.
To measure bliss points i and policy positions ~Di and ~Ri , I use survey items that ask each
respondent to place self, Republican party and Democratic party on seven-point scales referring
either to liberal-conservative ideology or to a policy issue. The single policy dimension in Alesina
and Rosenthal's model is supposed to summarize all possible grounds for electoral competition,
so I use as broad range of scale items as possible.7 In each year I use every scale for which
survey respondents were asked to place themselves and the two parties. To combine the scales into
single variables, I use the idea that it is relative rather than absolute positions that matter for the
coordinating voting model. What matters for each self placement item is the proportion of the
population that would support each of the seven possible positions. I assume that the items are, at
least to a rough approximation, stochastically ordered relative to a common underlying distribution
of positional preferences. I use the cumulative distributions observed for each set of three scales
to compute numerical codes for the response categories. By the logic of Alesina and Rosenthal's
theoretical analysis, such codes are comparable across issues, because the theory depends only on
the parties' locations relative to the distribution of voters.
The Appendix lists the items used for each survey and describes in more detail the method used
to scale the responses into the [0; 1] interval. I assume that the survey responses reect individuals'
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beliefs about the powers of the President and the Congress, so that the observed party position
data are ~Di and ~Ri rather than Di and Ri . For each individual the value of each bliss point or
party position variable is an average taken over all the items to which the individual responded.
To measure vote choices I use the post-election choices reported by individuals who said they
voted. In the zijk terms I include a set of dummy variables to measure partisanship, a variable to
measure retrospective evaluations of the national economy, dummy variables to measure whether
a Democratic or Republican incumbent is running for reelection or whether there is an open seat,
and a dummy variable for each year. The incumbent status variables are included in such a way
as to aect only the choice between House candidates. I also include measures of the absolute
dierences between each individual's bliss point and the positions the individual perceives for the
jk
Democratic and Republican parties. Using coecients cjk
h for h 2 P = fSD,D,DL,I,RL,R,SRg, cm
jk
for m 2 Y = f84,88,92,96g, cjk
E , c1, c2 , c3 , c4, c5 and c6, the zi terms are
X DR
X DR
ch PID hi +
cm YRmi + cDR
E PRESi ECi + c1ji , ~Di j + c2 ji , ~Ri j
h2P
m2Y
X
X RD
ziRD = cRD
cm YRmi + cRD
E PRESi ECi + c3ji , ~Di j + c4 ji , ~Ri j
h PID hi +
h2P
m2Y
ziDR =
+ c5IDEMi + c6 IREPi
ziDD =
X DD
X DD
ch PIDhi +
cm YR mi + cDD
E PRESi ECi + c1ji , ~Di j + c2ji , ~Ri j
h2P
m2Y
+ c3ji , ~Di j + c4 ji , ~Ri j + c5IDEMi + c6 IREPi
with ziRR = 0 to normalize the coecients. PIDSD i , PIDD i , PIDDL i , PIDI i , PIDRL i , PIDR i and
PIDSR i are the partisanship dummies.8 YR84i , YR88i , YR92i and YR96 i are dummy variables
for each indicated year. ECi measures economic evaluations.9 PRESi changes sign depending
on the incumbent President's party: PRESi = 1 if Republican; = ,1 if Democrat. I use the
RD
DD
product PRESi ECi so that cDR
E , cE and cE should all be negative: if voters are using a simple
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retrospective calculus keyed to the incumbent President, then an increase in PRESi ECi should
increase the chances of voting for the Republican candidate. Dummy variables IDEMi and IREPi
measure incumbent status. IDEMi = 1 if a Democratic incumbent is running for reelection in
individual i's congressional district, while IREPi = 1 if a Republican incumbent is running for
reelection there. If both IDEMi = 0 and IREPi = 0, the district has an open seat.10
In the beta densities fGi and fVi , xi is ECi and b1 and b2 are parameters to be estimated.
A value of b1 < 0 would support the idea that an individual who thinks the economy is getting
worse (ECi < 0) coordinates her votes in a way that more strongly favors Democratic presidential
candidates, while someone who believes the economy is getting better (ECj > 0) coordinates in a
way that more strongly favors Republicans. If b1 < 0, then gi > gj , so that if individuals i and
~
~
j place the parties in the same locations (~Di = ~Dj < ~Ri = ~Rj ), then ^i > Di +2 Ri ^j .
The economic pessimist, i, has an expected presidential cutpoint greater than that of the economic
optimist, j . The conditions under which the pessimist will also have a legislative cutpoint greater
than that of the optimist are more complicated, as the result depends on the values of b1 , b2, ~Di
and ~Ri .11
Estimation and Test Results
Estimation is by maximum likelihood, using only individuals who have ~Di < ~Ri and who voted for
either the Democratic or Republican candidate in both the presidential and House races.12 Table
1 reports the maximum likelihood estimates (MLEs) for the parameters. The likelihood ratio test
statistic for the signicance of the coordinating structure is X^ 2 = 15:86. Using the 25 distribution,
the null hypothesis that the coordinating structure makes no dierence gives prob(X 2 > X^ 2) < :01.
Clearly the coordinating structure matters.13
*** Table 1 about here ***
10
The coordinating pattern depends strongly on the individual's evaluation of the national economy. The MLE for b1 is a hefty ^b1 = ,7:10. By a one-tailed t-test, the estimate is signicantly
negative. The eect the negative value of b1 has on the beta densities fGi (presidential cutpoint)
and fVi (voter location) can be seen in Figure 1. When the evaluation of the economy is \much
worse," both densities have probability mass concentrated on high values. The voter location density is practically a point mass for the value v = 1:0. As the evaluation of the economy improves
through \worse" and \same" to \better," the densities atten out, in the direction of uniformity.
The density for an economic evaluation of \much better" is in each case not noticeably dierent
from the the density for \better."
*** Figure 1 about here ***
For most values of ~Di and ~Ri , the estimated densities imply that the cutpoints vary in the
expected way as the economic evaluation varies, albeit with one signicant deviation. The plots
in Figure 2 illustrate the typical pattern. Figure 2 shows the probabilities RR , DR , RD and
DD simulated for each bliss point value in the unit interval, with ~Di = :2, ~Ri = :8 and z RR =
zDR = z RD = zDD = 0. For an economic evaluation of \better," the expected presidential cutpoint
is ^i :5 while the expected legislative cutpoint is ~i :4 (Figure 2(a)). As one would expect
given the similarity of the densities in Figure 1, the same result occurs for an economic evaluation of
\much better" (not shown). When the evaluation of the economy decreases to \same," the expected
presidential cutpoint does not change but the expected legislative cutpoint increases to ~i :5
(Figure 2(b)). Upon a further decrease of the evaluation, to \worse," the expected presidential
cutpoint increases to ^i :55, but the expected legislative cutpoint increases more, to ~i :6
(Figure 2(c)). When the evaluation reaches the maximum of pessimism, with the individual saying
that over the past year the economy has become \much worse," the expected presidential cutpoint
increases to ^i :73, but the expected legislative cutpoint plummets to ~i :38 (Figure 2(d)).
11
*** Figure 2 about here ***
The simulated probabilities in Figure 2 illustrate three characteristic features of the coordinating
structure. First, the coordinating structure has no eect on an individual's vote for an oce when
the individual's bliss point equals the expected cutpoint for that oce. If the bliss point equals
DR
DD
RD
the expected presidential cutpoint (i = ^i ), then RR
i = i and i = i , and if the bliss
DR
RR
RD
point equals the expected legislative cutpoint (i = ~i ), then DD
i = i and i = i . The
reason why all four probabilities are equal in Figure 2 whenever the bliss point equals the expected
presidential cutpoint is that the MLE for aH is the boundary value ^aH = 0. The second feature is
RD
that for bliss points between the expected cutpoints, DR
i > i when the expected presidential
DR
cutpoint is greater than the expected legislative cutpoint, but RD
i > i when the expected
presidential cutpoint is less than the expected legislative cutpoint. Third, the probability of a split
ticket vote increases as the distance between the expected cutpoints increases. In Figure 2, the
probability of a split ticket vote signicantly exceeds the highest probability for a straight ticket
vote only in Figure 2(d), where the evaluation that the economy has become \much worse" induces
a fairly wide separation between ^i and ~i .
Figure 3 shows that, throughout the data from all ve election years, widely separated cutpoints
occur only for individuals who say the economy has become \much worse." Without exception,
these people have ^i > ~i . For these people the most likely split ticket vote, at least as far as the
coordinating structure is concerned, is for a Democratic presidential candidate and a Republican
candidate for the House. Of course, the contributions to the vote choice from the coordinating
structure may well be outweighed by the eects of the factors in ziDR , ziRD and ziDD . In any case,
in view of the magnitudes estimated for the coordinating structure coecients, coordinating voting
cannot be expected substantially to increase the chances of split ticket voting by voters who do
not hold to a seriously negative reading of recent economic performance. The largest dierence
12
between expected cutpoints for those who evaluate the economy as \better" or \much better" is
^i , ~i =.16. The univariate, normal-theory 95% condence intervals for the coordinating structure
coecients are :03 aP 1:42, 0 aH 0:55, and :90 aPH 5:65. Using the upper bounds
for each of these intervals and, for instance, i = :5, ^i = :58 and ~i = :42, would give a value of
wi = f,:07; :19; ,:12; :07g for the coordinating structure, a value easily outweighed by factors in
ziDR , ziRD and ziDD .
*** Figure 3 about here ***
The Democrats' Dilemma
The pattern of coordinating voting that depends on an individual's evaluation of the economy
creates a major dilemma for Democratic candidates. The coordinating voting pattern implies that
voters punish a Democratic President for success in improving the economy.
Figures 4 and 5 show simulations of the vote in which, unlike Figure 2, the factors in ziDR , ziRD
and ziDD are not all set to zero. Results appear for ve of the levels of partisanship. In Figure 4 the
evaluation of the economy is \much worse" while in Figure 5 the evaluation is \much better." The
values used for the party policy positions are ~Di = :2 and ~Ri = :8. The simulated eects include
the eects of the terms of ziDR , ziRD and ziDD that involve the absolute dierences ji , ~Di j and
ji , ~Rij.14 Each gure shows probabilities simulated once assuming that a Republican is President
and once assuming that the President is a Democrat. Because of the use of the product PRESi ECi
in estimating the direct eect of economic evaluations, the probabilities for a Republican President
with a \much worse" economy are quite similar to the probabilities for a Democratic President
with an economy that is evaluated as \much better." Likewise, the probabilities for a Democratic
President with a \much worse" economy are similar to the probabilities for a Republican President
with an economy rated as \much better." Indeed, the coordinating structure is the only reason
13
why the respective sets of probabilities are not identical. As previously discussed, the expected
cutpoints are not the same for the two dierent evaluations of the economy. In going from an
evaluation of \much worse" to one of \much better," the expected legislative cutpoint increases
slightly, from ~i :38 to ~i :4, while the expected presidential cutpoint drops dramatically, from
^i :73 to ^i :5.
*** Figures 4 and 5 about here ***
Figure 6 shows the magnitude of the electoral problem that the coordinating pattern implies for
Democratic Presidents. The gure shows two sets of dierences between the simulated probabilities
of Figures 4 and 5. In the left column of Figure 6, the probabilties for a Republican President with
an economic evaluation of \much worse" are subtracted from the probabilities for a Democratic
President with an economic evaluation of \much better." In the right column, the probabilties
for a Democratic President with an economy evaluated as \much worse" are subtracted from the
probabilities for a Republican President with an economic evaluation of \much better." The point of
these values is to illustrate what happens to individuals' voting probabilities when an economically
unsuccessful President of one party is succeeded by a President of the other party who enjoys better
economic results. Presumably, in that case, many individuals who judged the economy to be in
severe decline at the time of the election in which the economically unsuccessful President was
turned out of oce will have converted to have a much more favorable view of how things are going
at the time when the seemingly more skillful successor stands for reelection.
*** Figure 6 about here ***
Figure 6 shows that a Democratic President who delivers economic results that voters nd
to be much better than what occurred with an economically unsuccessful Republican predecessor
for the most part loses rather than gains electoral support. Among voters who have liberal policy
preferences, meaning a bliss point i < :2, the probability DR
i of a vote split Democratic-President14
Republican-House-candidate increases very slightly|by about .02. But this seeming gain for the
Democratic President is actually a loss, as the increases in DR
i are more than oset by decreases
in the probability of a Democratic straight ticket vote. In fact what is happening is that the
Democrats are losing support in open seat House races. Among moderate and conservative voters
(i > :3) the Democrats' situation is even more perversely punitive, as both the straight ticket
DR
(DD
i ) and split ticket (i ) probabilities of a vote for the Democratic President decrease. The
losses occur for all kinds of partisans. Among conservative Democrats and moderate Independents,
the reduction in the probability of a vote for the Democratic President totals almost .1.
In sharp contrast, Figure 6 shows that a Republican President who delivers much better economic performance than an unsuccessful Democratic predecessor for the most part gains rather
than loses support. In the left column of Figure 6, the change in the probability RR
i of a Republican straight ticket vote is uniformly positive. The probability of a vote split Republican-PresidentDemocratic-House-candidate does not always increase, but when it decreases the reduction is always
oset by the increase in RR
i .
What may be described as the economic bias in the structure of coordinating voting presents
a Democratic President with a serious political dilemma. To fail to improve the economy would
mean overwhelming electoral defeat. In addition, to pursue economic stagnation would be inherently wrong. Stagnation cannot be a policy goal. But success in increasing growth, expanding
employment, raising wages and stabilizing prices would mean losses rather than gains in electoral
support. The Democratic President is in a box.
The Democratic President can avoid losses only if the voters who rate the economy as having
improved also believe that the Democratic party policy position has shifted to the right. Voters
must believe that the Democratic party has become more conservative. By the current parameter
MLEs, the expected presidential cutpoint for a voter who thinks the economy has improved is
15
^i (~Di +~Ri )=2, midway between the parties' policy positions. The expected presidential cutpoint
for a voter who thinks the economy has seriously declined is quite near the policy position of the
Republican party (gi :875). If the policy position of the Republican party remains unchanged
between elections, then to avoid a loss from improving the economy, the Democratic President must
make moderate and conservative voters believe that the policy position of the Democratic party has
shifted a substantial distance toward the position previously taken by the Republican party. To avoid
losing support, the Democratic President shift the perceived policy position of the Democratic party
to the center, and perhaps somewhat to the right of center. If the Republican party policy position
itself shifts to the right, then the Democratic party policy position does not need to change as much
to preserve the Democrat's previous level of electoral support. For in this case, the Republican
party's rightward movement will help keep (~Di + ~Ri )=2 near the location of the previous expected
presidential cutpoint, without the Democratic party's position having to change by as much. But
if the Republican party policy position shifts to the left, the Democratic party policy position must
shift further to the right than would have been necessary to preserve its support had the Republican
position remained unchanged.
You are Bill Clinton. It is 1996 and the economy is strong. Your Republican opponent, Bob Dole,
is resisting pulls from inside his party to move farther right. Indeed, in some respects he may be
edging slightly to the left. You have on your desk a bill that will abolish welfare, mandate workfare,
reduce health insurance protection for poor children and cut programs for infant nutrition. Signing
the bill will provoke outrage among core Democratic constituencies, who will be appalled by an
action they believe will cause one million children to fall into poverty. You suspect that, if you sign
the bill, long-time liberal friends and allies who work on children's issues in your administration will
resign in protest, condemning you for having caved in to the far right wing of House Republicans.
You heave a silent sigh of thanks to Newt Gingrich. Smiling in anticipation, you lift your pen.
16
Appendix
To measure an individual's bliss point and perceived positions for the Republican and Democratic
parties, I assign codes in the [0; 1] interval to each scale in a collection of sets of three seven-point
placement scales and then average over the sets to which the individual responded to all three
scales|one for self and one for each party. Table 2 lists descriptions and variable numbers for the
survey items used for each year. All items are oriented so that the \liberal" position, normally associated with the Democratic party during the given time period, has the lower number. To determine
numerical codes, I start by computing the cumulative response proportions for the three scales for
each survey item. Denote the successive proportions for item k (e.g., k = \Liberal/Conservative")
by 0 = r0kj r1kj r6kj r7kj = 1, for j 2 fS; D; Rg, respectively for the self, Democratic
k + rk + rk )=3.
party and Republican party scales. For all m 2 f0; 1; : : :; 7g I compute rmk = (rmS
mD mR
The numerical code for each of the m 2 f1; : : :; 7g original survey responses on all three scales of
type k is rmk = (rmk ,1 + rmk )=2. Table 3 shows the codes computed by this procedure for each survey
item.
*** Table 2 and Table 3 about here ***
17
Notes
1. Let random variable ^ be distributed beta ( ; ) with mean ^0 = =( + ), 0 < ^0 < 1.
In terms of and ^0 the variance is var(^ ) = (1 , ^0 )^02 =( + ^0 ), so that for xed mean ^0 ,
@ var(^ )=@ = ,(1 , ^0)^02=( + ^0 )2 < 0. Because = (1 , ^0)=^0, increasing
for xed ^0
R~
increases . It follows that for any ^i0 = ~DiRi ^f0^ i (^)d^ generated by G0 i < 1 or 0Gi < 1, there is a
value G00 i > 1 such that Gi > 1 and ^i = ^i0 but var(^) < var(^0 ) for all Gi > G00 i .
2. The mode is exp(b1xi )=(exp(b1xi ) + 1) (Johnson, Kotz and Balakrishnan 1995, 219).
3. The beta (1,1) distribution is the uniform distribution.
4. Recall that gi(^) maps ^ 2 [~Di ; ~Ri] onto the unit interval [0; 1].
5. The specication with Vi as in the text and Vi = exp(b4)+1 for unknown coecient b4 is not
identied in our data. Identication similarly fails if Vi = exp(b4)+1 and Vi = exp(b3xi + b4 )+1.
Because the likelihood depends only on the integral Pi (^), a free parameter b4 is redundant with
b2. There is information to identify the variance for only one of the densities, either fVi or fGi but
not both.
6. Alesina and Rosenthal (1995, 73) assume that voter utility functions are single peaked and
dierentiable and that voter preferences satisfy a Spence-Mirlees single-crossing property. In the
present model each individual's expected utility may have these properties, in that the multinomial logit likelihood is compatible with stochastic utility maximizing behavior (McFadden 1974;
McFadden 1978; Borsch-Supan 1990).
7. There is evidence that the political parties usually represent the principal opposing bundles
of policy positions in American politics (Poole and Rosenthal 1984; 1997).
8. The dummies mark the levels of the standard ANES party identication item: PIDSDi = 1,
strong Democrat; PIDD i = 1, Democrat; PIDDL i = 1, independent Democratic leaner; PIDIi = 1,
pure Independent; PIDRL i = 1, independent Republican leaner; PIDR i = 1, Republican; and
18
PIDSR i = 1, strong Republican. The ANES variable numbers for each year are 266 (1980), 866
(1984), 274 (1988) and 3634 (1992).
9. For 1980, 1984 and 1988 the question wording is \What about the economy? Would you say
that over the past year the nation's economy has gotten better, stayed about the same, or gotten
worse?" For 1992 the initial part of the question changed to read, \How about the economy."
For 1996 the initial part was \Now thinking about the economy in the country as a whole." The
responses are coded \much worse" (,1), \somewhat worse" (,:5), \same" (0), \somewhat better"
(.5) and \better" (1). The ANES variable numbers for each year are 150 (1980), 228 (1984), 244
(1988), 3532 (1992) and 960386 (1996).
10. The ANES variable numbers for each year are 740 (1980), 59 (1984), 50 (1988), 3021 (1992,
with errors corrected as indicated in the codebook le nes92int.cbk) and 960097 (1996).
11. That the conditions are complicated is clear from
#
Z ~Ri
Pi log(^)f^ i (^)d^
Gi + Gi ) , (Gi ))Pi +
~Di
)
Z ~Ri Z 1
+ (( Vi + 1) , (1))Pi +
log(v )fVi dvf^ i (^)d^
~
^
(
"
@ Pi = x expfb x g expfb2g ((
1 i ~
@b1 i
Ri , ~Di
Di
where (z ) = d log ,(z )=dz denotes the logarithmic derivative of the gamma function. The terms
(( Gi + Gi ) , (Gi ))Pi and (( Vi +1) , (1))Pi are positive but the integrals are both negative.
12. Of those survey respondents with complete data who voted as described in the text, 136
(2.9 percent) had ~Di = ~Ri and 554 (11.8 percent) had ~Di > ~Ri .
13. Adding Jacobson's (1990) measure of challenger quality made no dierence in estimates
computed using the data for 1980{92. The LR test statistic for the hypothesis that the quality
variable adds nothing is X^ 2 = 4:33, which by the 22 distribution is not signicant.
14. YR84i = YR88i = YR92i = YR96i = IDEMi = IREPi = 0, so the simulations are for
probabilites as of 1980 in an open seat House race.
19
References
Alesina, Alberto. 1987. \Macroeconomic Policy in a Two-party System as a Repeated Game."
Quarterly Journal of Economics 102:651{678.
Alesina, Alberto. 1988. \Credibility and Policy Convergence in a Two-party System with Rational
Voters." American Economic Review 78:796{806.
Alesina, Alberto, John Londregan and Howard Rosenthal. 1993. \A Model of the Political
Economy of the United States." American Political Science Review 87:12{33.
Alesina, Alberto, and Howard Rosenthal. 1989. \Partisan Cycles in Congressional Elections and
the Macroeconomy." American Political Science Review 83:373{398.
Alesina, Alberto, and Howard Rosenthal. 1995. Partisan Politics, Divided Government, and the
Economy. New York: Cambridge University Press.
Borsch-Supan, Axel. 1990. \On the Compatibility of Nested Logit Models with Utility Maximization." Journal of Econometrics 43:373{388.
Brody, Richard A., and Paul M. Sniderman. 1977. \From Life Space to Polling Place: The
Relevance of Personal Concerns for Voting Behavior." British Journal of Political Science
7:337{360.
Jacobson, Gary C. 1990. The Electoral Origins of Divided Government: Competition in U.S.
House Elections, 1946{1988. Boulder: Westview.
Johnson, Norman L., Samuel Kotz and N. Balakrishnan. 1995. Continuous Univariate Distributions, Volume 2. 2d ed. New York: John Wiley & Sons.
20
McFadden, Daniel. 1978. \Modelling the Choice of Residential Location." In Anders Karlqvist,
Lars Lundqvist, Folke Snickars and Jorgen W. Weibull, eds., Spatial Interaction Theory and
Planning Models. New York: North-Holland, pp. 75{96.
Miller, Warren E., and the National Election Studies. 1982. American National Election Study,
1980: Pre- and Post-Election Survey [computer le]. Ann Arbor, MI: Center for Political
Studies, University of Michigan. [original producer]. 2nd ICPSR ed. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [producer and distributor].
Miller, Warren E., and the National Election Studies. 1986. American National Election Study,
1984: Pre- and Post-Election Survey [computer le]. Ann Arbor, MI: Center for Political
Studies, University of Michigan. [original producer]. 2nd ICPSR ed. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [producer and distributor].
Miller, Warren E., and the National Election Studies. 1989. American National Election Study,
1988: Pre- and Post-Election Survey [computer le]. Ann Arbor, MI: Center for Political
Studies, University of Michigan. [original producer]. 2nd ICPSR ed. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [producer and distributor].
Miller, Warren E., Donald R. Kinder, Steven J. Rosenstone, and the National Election Studies.
1993. American National Election Study, 1992: Pre- and Post-Election Survey [enhanced
with 1990 and 1991 data] [computer le]. Conducted by University of Michigan, Center for
Political Studies. ICPSR ed. Ann Arbor, MI: University of Michigan, Center for Political
Studies, and Inter-university Consortium for Political and Social Research [producers]. Ann
Arbor, MI: Inter-university Consortium for Political and Social Research [distributor].
Poole, Keith T., and Howard Rosenthal. 1984. \U.S. Presidential Elections 1968{80: A Spatial
Analysis." American Journal of Political Science 28:282{312.
21
Poole, Keith T., and Howard Rosenthal. 1997. Congress: A Political-economic History of Roll
Call Voting. New York: Oxford University Press.
Rosenstone, Steven J., Donald R. Kinder, Warren E. Miller, and the National Election Studies.
1997. American National Election Study, 1996: Pre- and Post-Election Survey [Computer
le]. 2nd release. Ann Arbor, MI: University of Michigan, Center for Political Studies [producer], 1997. Ann Arbor, MI: Inter- university Consortium for Political and Social Research
[distributor].
Schlozman, Kay Lehman, and Sidney Verba. 1979. Injury to Insult: Unemployment, Class and
Political Response. Cambridge: Harvard University Press.
22
Table 1: Coordinating Vote Model Parameter Estimates
parameter MLE SE
b1
{7.10 4.03
b2
{4.17 5.40
parameter MLE SE
aP
.72 .35
aH
.00 .28
aPH
3.27 1.21
cDR
SD
cDR
D
cDR
DL
cDR
I
cDR
RL
cDR
R
cDR
SR
cDR
E
cDR
84
cDR
88
cDR
92
cDR
96
cRD
SD
cRD
D
cRD
DL
cRD
I
cRD
RL
cRD
R
cRD
SR
cRD
E
cRD
84
cRD
88
cRD
92
cRD
96
2.08
.18
.82
{1.11
{2.00
{2.11
{3.42
{1.14
{.57
{.52
.12
.25
.50
.31
.37
.44
.34
.32
.44
.20
.32
.33
.36
.32
1.01
{.06
.45
{.25
{1.12
{1.25
{1.87
{.26
{.01
{.03
{.04
{1.01
.53
.28
.34
.32
.26
.26
.27
.15
.19
.23
.26
.29
parameter MLE
d1
{4.14
d2
4.72
d3
{.89
d4
1.19
d5
1.30
d6
{1.25
DD
cSD
3.67
cDD
1.14
D
cDD
1.78
DL
cDD
{.74
I
DD
cRL
{2.56
cDD
{2.50
R
cDD
{3.95
SR
cDD
{1.37
E
cDD
{.44
84
cDD
{.12
88
cDD
{.17
92
DD
c96
.34
Note: Maximum likelihood estimates. n = 3644 cases. Log-likelihood = ,2209:95.
SE
.71
.70
.48
.46
.16
.17
.50
.30
.36
.37
.35
.34
.44
.17
.26
.28
.32
.29
Table 2: Variables Used to Measure Bliss Points and Party Policy Positions
year description
1980 Liberal/Conservative
Defense Spending
Government Services/Spending (reversed)
Reduce Ination/Reduce Unemployment
Liberal/Conservative Views
Government Aid to Minorities
Getting Along with Russia
Equal Rights for Women Scale
Government Guaranteed Job and Living Standard
1984 Liberal/Conservative Placement
Liberal/Conservative
Government Services/Spending (reversed)
Minority Aid/No Aid
Involvement in Central America
Defense Spending
Social/Economic Status of Women
Cooperation with Russia
Guaranteed Standard of Living/Job
1988 Liberal/Conservative
Government Services/Spending (reversed)
Defense Spending
Government-Funded Insurance
Guaranteed Standard of Living/Job
Social/Economic Status of Blacks
Social/Economic Status of Minorities
Cooperation with Russia
Women's Rights
1992 Ideological Placement
Government Services/Spending (reversed)
Defense Spending
Job Assurance
1996 Liberal/Conservative
Government Services/Spending (reversed)
Defense Spending
Abortion
Jobs/Environment
Environmental Regulation
variable numbers
267, 278, 279
281, 286, 287
291, 296, 297
301, 306, 307
1037, 1053, 1054
1062, 1073, 1074
1078, 1089, 1090
1094, 1105, 1106
1110, 1121, 1122
119, 120, 121, 131, 132,
133, 135, 136, 137
369, 373, 374
375, 378, 379
382, 385, 386
388, 391, 392
395, 398, 399
401, 404, 405
408, 411, 412
414, 417, 418
228, 234, 235
302, 307, 308
310, 315, 316
318, 321, 322
323, 328, 329
332, 337, 338
340, 345, 346
368, 373, 374
387, 390, 391
3509, 3517, 3518
3701, 3704, 3705
3707, 3710, 3711
3718, 3721, 3722
960365, 960380, 960379
960450, 960462, 960461
960463, 960478, 960477
960503, 960518, 960517
960523, 960536, 960535
960537, 960542, 960541
Note: Variable numbers for 1980{92 refer to data on the \ANES 1948-1994 CD-ROM" release of
May, 1995.
Table 3: Item Response Scores Used for Each Set of Bliss Point and Policy Position Components
year description
original
1980 L/C
DS
GS/S(r)
RI/RU
L/CV
GAtM
GAwR
ERfWS
GGJaLS
1984 L/CP
L/C
GS/S(r)
MA/NA
IiCA
DS
S/ESoW
CwR
GSoL/J
1988 L/C
GS/S(r)
DS
G-FI
GSoL/J
S/ESoB
S/ESoM
CwR
WR
1992 IP
GS/S(r)
DS
JA
1996 L/C
GS/S(r)
DS
A
J/E
ER
item response scores
1
2
.0175
.103833
.0158333 .0585
.0606667 .204
.0216667 .0876667
.0103333 .0841667
.0285
.1115
.0381667 .120333
.0956667 .283167
.0408333 .143667
.0908333 .243
.0211667 .12
.0433333 .153
.0421667 .146833
.0348333 .138333
.032
.115667
.0431667 .148
.0401667 .132667
.0386667 .133333
.0206667 .113667
.0395
.151333
.029
.102
.0573333 .169167
.0323333 .110833
.0306667 .104667
.04
.127667
.0446667 .1505
.1135
.302
.0246667 .126667
.0436667 .156667
.0321667 .123667
.0336667 .120667
.0201667 .114333
.033
.132333
.0165
.0808333
.0621667 .285167
.0316667 .126333
.0401667 .1485
3
.250833
.143
.382333
.225167
.229333
.253167
.235333
.469
.2925
.365167
.270833
.31
.300833
.293667
.24
.2985
.264667
.271667
.257
.322167
.2155
.297333
.231833
.228667
.261833
.318333
.468667
.280333
.318
.2825
.2495
.2655
.2965
.218
.5505
.291333
.324833
4
.432833
.298833
.581833
.4645
.421833
.459333
.42
.6645
.485
.440667
.454167
.516833
.517
.485333
.431
.531333
.468333
.4745
.434667
.552167
.4215
.472833
.423333
.452667
.471167
.569833
.6785
.463667
.535333
.524167
.432833
.446833
.506167
.438833
.8275
.527833
.557167
Note: See Table 2 for full identication of the variables.
5
.6315
.517333
.7665
.718667
.633
.685667
.658333
.828833
.680333
.536
.6505
.7115
.727333
.678667
.650167
.757333
.687333
.69
.628833
.765667
.660833
.657333
.6335
.6775
.684667
.7895
.849333
.652
.741833
.754
.631667
.638833
.708333
.693
6
.842333
.751167
.896833
.883833
.852833
.857
.8585
.924667
.842167
.6975
.843833
.858833
.868667
.835
.8295
.889167
.848
.8555
.826667
.8975
.840167
.809333
.806333
.817667
.837
.902167
.930333
.830333
.8805
.8925
.796833
.837333
.8675
.8865
7
.979167
.932333
.973167
.9705
.986167
.9605
.967
.978833
.957167
.889167
.9755
.963833
.962167
.9515
.954
.9695
.956833
.963
.9685
.973667
.958333
.939333
.942833
.938167
.949333
.97
.979333
.963667
.969
.971667
.9355
.974
.968167
.978667
.755
.7625
.903167 .979333
.8975 .975667
Figure 1: Presidential Cutpoint and Voter Densities, by National Economy Evaluation
much worse
density
0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
presidential cutpoint
voter location
worse
worse
0.8
1.0
0.8
1.0
0.8
1.0
0.8
1.0
0.8
1.0
20
density
0.8
0.4
0
0.0
10
1.2
30
0.0
density
100
10
0
5
density
15
300
much worse
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
presidential cutpoint
voter location
same
same
0.0
1.0
density
0.98
0.94
density
2.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
presidential cutpoint
voter location
better
better
0.85
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
voter location
much better
much better
1.001
presidential cutpoint
density
0.96
0.995
0.92
0.998
0.2
1.00
0.0
density
0.95
density
0.96
0.92
density
1.00
0.0
0.0
0.2
0.4
0.6
presidential cutpoint
0.8
1.0
0.0
0.2
0.4
0.6
voter location
Figure 2: Examples of Vote Choice Probabilities Generated by the Coordinating Structure
0.2
0.4
0.8
0.4
1.0
0.0
0.2
0.4
0.6
0.8
1.0
individual’s bliss point
(c) Economy is Worse
(d) Economy is Much Worse
0.0
0.2
0.4
0.6
0.4
θ̃
0.0
θ̂ θ̃
0.2
0.2
probability
0.4
0.6
individual’s bliss point
0.0
probability
θ̃θ̂
0.0
0.6
0.6
0.0
0.2
probability
0.4
0.2
θ̃ θ̂
0.0
probability
0.6
(b) Economy is the Same
0.6
(a) Economy is Better
0.8
1.0
individual’s bliss point
0.0
0.2
0.4
θ̂
0.6
0.8
1.0
individual’s bliss point
Republican straight ticket
Democratic presidential, Republican House candidate
Republican presidential, Democratic House candidate
Democratic straight ticket
Party policy positions are ~D = :2 and ~R = :8. In this gure, ^ denotes the expected presidential
cutpoint and ~ denotes the expected legislative cutpoint.
Figure 3: Expected Cutpoints Estimated for Individuals in the ANES Presidential Year Samples,
1980{96, by Each Individual's Evaluation of the National Economy
0.6
0.8
•
0.4
•
•••
••
0.2
legislative cutpoint
0.4
0.6
•
•
•••
•
•
•
•
•
•
••
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••••••••••••••
••••••••••
•••• ••
0.0
•
0.0
0.2
legislative cutpoint
0.8
1.0
worse
1.0
much worse
0.0
0.2
0.4
0.6
0.8
1.0
0.0
presidential cutpoint
0.2
0.4
0.6
0.8
1.0
presidential cutpoint
0.4
0.6
••
•• •
••••••••••••
•••••••••••••••••••
•
•
•
•
•
•••
••••••••••••••••
••••••••••••••••••••••
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presidential cutpoint
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legislative cutpoint
0.8
1.0
much better
1.0
better
0.0
0.2
0.4
0.6
0.8
presidential cutpoint
1.0
0.0
0.2
0.4
0.6
0.8
presidential cutpoint
1.0
0.6
0.8
0.4
θ̂
0.6
0.8
θ̂
0.4
0.6
0.8
1.0
0.0
0.2
0.4
θ̂
0.6
0.8
Independent
Independent
θ̂
0.6
0.8
θ̃
0.0
0.6
0.4
1.0
0.0
0.2
0.4
θ̂
0.6
0.8
Republican
Republican
0.0
θ̂
0.6
0.8
θ̃
0.0
0.6
0.4
1.0
0.0
0.2
0.4
θ̂
0.6
0.8
Strong Republican
Strong Republican
0.0
θ̂
0.6
0.8
1.0
straight Republican ticket,
Republican president, Democratic House,
θ̃
0.0
0.6
0.4
1.0
0.6
individual’s bliss point
probability
individual’s bliss point
0.2
1.0
0.6
individual’s bliss point
probability
individual’s bliss point
0.2
1.0
0.6
individual’s bliss point
probability
individual’s bliss point
0.2
1.0
0.6
θ̃
0.0
probability
0.6
0.2
individual’s bliss point
Legend:
0.2
Democrat
θ̃
0.0
0.0
Democrat
θ̃
0.0
0.6
1.0
individual’s bliss point
θ̃
0.0
θ̃
0.0
probability
0.6
θ̂
0.4
individual’s bliss point
0.0
probability
0.2
0.0
probability
Strong Democrat
θ̃
0.0
probability
Democratic President
Strong Democrat
θ̃
0.0
probability
Republican President
0.0
probability
Figure 4: Vote Choice Probabilities for \Much Worse" Economy, Open Seat
0.0
0.2
0.4
θ̂
0.6
0.8
1.0
individual’s bliss point
Democratic president, Republican House,
straight Democratic ticket.
0.6
0.8
0.4
0.6
0.8
0.2
0.4
0.6
0.8
0.6
1.0
0.0
0.2
0.4
0.6
0.8
Independent
Independent
0.6
0.8
θ̃ θ̂
0.0
0.6
0.4
1.0
0.0
0.2
0.4
0.6
0.8
Republican
Republican
0.6
0.8
0.0
θ̃ θ̂
0.0
0.6
0.4
1.0
0.0
0.2
0.4
0.6
0.8
Strong Republican
Strong Republican
0.6
0.8
1.0
0.0
straight Republican ticket,
Republican president, Democratic House,
θ̃ θ̂
0.0
0.6
0.4
1.0
0.6
individual’s bliss point
probability
individual’s bliss point
0.2
1.0
0.6
individual’s bliss point
probability
individual’s bliss point
0.2
1.0
0.6
individual’s bliss point
probability
individual’s bliss point
0.2
1.0
θ̃ θ̂
0.0
0.6
probability
Democrat
individual’s bliss point
Legend:
0.2
Democrat
θ̃ θ̂
0.0
0.0
individual’s bliss point
θ̃ θ̂
0.0
0.6
1.0
θ̃ θ̂
0.0
θ̃ θ̂
0.0
probability
0.6
0.4
individual’s bliss point
0.0
probability
0.2
0.0
probability
Strong Democrat
θ̃ θ̂
0.0
probability
Democratic President
Strong Democrat
θ̃ θ̂
0.0
probability
Republican President
0.0
probability
Figure 5: Vote Choice Probabilities for \Much Better" Economy, Open Seat
0.0
0.2
0.4
0.6
0.8
1.0
individual’s bliss point
Democratic president, Republican House,
straight Democratic ticket.
Strong Democrat
0.8
1.0
0.2
0.4
0.6
0.8
0.4
0.6
0.8
-0.10
probability
0.2
1.0
0.0
0.2
0.4
0.6
0.8
Independent
Independent
0.6
0.8
-0.10
probability
0.4
1.0
0.0
0.2
0.4
0.6
0.8
individual’s bliss point
Republican
Republican
0.6
0.8
-0.10
probability
-0.10
0.4
1.0
0.0
0.2
0.4
0.6
0.8
individual’s bliss point
Strong Republican
Strong Republican
0.6
0.8
1.0
straight Republican ticket,
Republican president, Democratic House,
-0.10
probability
0.4
individual’s bliss point
1.0
0.10
individual’s bliss point
0.2
1.0
0.10
individual’s bliss point
0.2
1.0
0.10
individual’s bliss point
0.10
individual’s bliss point
0.2
1.0
0.10
Democrat
0.10
Democrat
-0.10
0.0
Legend:
0.0
individual’s bliss point
0.10
0.0
-0.10
probability
0.6
0.10
0.0
probability
0.4
individual’s bliss point
-0.10
probability
0.0
probability
0.2
-0.10
probability
0.0
0.10
Better Republican President
Strong Democrat
0.10
Better Democratic President
-0.10
probability
Figure 6: Dierences in Vote Choice Probabilities, Open Seat
0.0
0.2
0.4
0.6
0.8
1.0
individual’s bliss point
Democratic president, Republican House,
straight Democratic ticket.